Personal life

As my bio says, I live a truly happy life. Sharing it with my family, girlfriend, friends and cats.

Professional career

I'm a mechanical engineer graduated from UFRGS in 2020. I've been programming since 2017.

I started studying data analysis in December 2020. Since then, I've been studying all stages of developing business solution models. Starting by understanding the business problem until creating models solutions, interfaces and deploying it through Clouds plataforms.

Some of my solutions so far includes detect cardiovascular diseases, detect bank transaction frauds - both by classification methods -, predict stores sales - by regression methods - and a deep exploratory analisys over housing market.

Contact:

In this project, I developed a Machine Learning model able to detect fraudulent transaction with 97,32% recall and 98,70% precision, over 6.3kk transactions.

This model performance, in the best case scenario, would create a company's revenue of $ 132,667,313.49.

In this project, I developed a Machine Learning model able to detect disease in early stages with 74,35% precision, over 70k patience.

This model performance, in the best case scenario, would increase a company's test profit from R$ 500,00 to R$ 2500,00. And would also replace a manual process by a machine learning model.

I also created a web page with Streamlit. Which predicts and return, with the user informations, his chances of having cardiovascular diseases.

Deploy: Here

In this project, I analyzed over 20k houses in Seattle city. I Speculated which houses the company should buy, which were the best renovation upgrades to do and when to sell them.

The business performance, in the best case scenario simulated, would create a company's revenue of $ 25,817,485.25 and a Return Over Investiment (ROI) of 92%.

I also created a web page with Streamlit. Which explain all hypotheses, business suggestions, business scenarios simulation and provides an interactive map to filter, by the user demands, what house to buy.

Deploy: Here

In this project, I developed a Machine Learning model able to predict 1115 stores next 6 week sales, with an average 7% MRSPE value for each daily sale prediction, with informations over 1115 stores trough 942 days.

This model performance, in the best case scenario, would predict all stores sales amount with a 2,87% error.

I also created a Telegram chatbot. Which return the expected, worst and best sales scenarios daily sales evolution graphs, for each store in the next 6 weeks.

Deploy: Here

Python	My SQL	Power BI	Heroku
Pandas	Numpy	scikit-learn	statsmodels
Matplotlib	Seaborn	Flask	Streamlit